Magnetic Resonance Imaging (MRI) is a powerful biomedical tool to non-invasively image the human body for disease diagnosis and healthy physiological process research. MRI is intrinsically slow, and numerous methods have been proposed to accelerate the MRI scan. One of the most important acceleration method is the under-sampling reconstruction technique (i.e., MR compressed sensing), where fewer samples are acquired in the MRI data space (k-space), and prior knowledge is used to restore the images. An image regularizer is used in reconstruction to reduce aliasing artifacts. The MRI image reconstruction problem is often formulated as an optimization problem with constraints, and iterative algorithms, such as non-linear conjugate gradient (NLCG), fast iterated shrinkage/thresholding algorithm (FISTA), alternating direction method of multipliers (ADMM), Broyden-Fletcher-Goldfarb-Shanno (BFGS) quasi-Newton method, or the like, are used to solve the optimization problem.
The amount and shape of aliasing artifacts introduced by the under sampling depend on which measurements are acquired in scanning. Changes in the acquisition sequence require changes in the reconstruction algorithm to deal with the amount and shape of the aliasing artifacts. In the optimization-based reconstruction, parameters of the reconstruction, such as the image regularizer, are manually tuned by the application developer. In data-driven approaches (e.g., machine-learnt reconstruction), the reconstruction is trained to optimize an image for a given sampling pattern. In either approach, a different reconstruction is used for different aliasing artifacts. Users are limited to the sampling patterns for which reconstruction is available or accept poorer reconstruction by using reconstruction not optimized to a desired sampling pattern.